Time Series Signal Analysis With Information Granulation Based on Permutation Entropy: An Application to Electroencephalography Signals
Why this work is in the frame
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Bibliographic record
Abstract
In this article, we reported a novel granulation method composed of complexity information based on permutation entropy (PeEn). This method aims to recognize the electroencephalography (EEG) patterns using this proposed granulation method. First, we define the complexity information for granular computing by a technique with fast calculation, i.e., PeEn. Then, the information granule can be constructed based on the time domain information, which completes complexity information. Together with the support vector machine algorithm, the proposed granulation method outperformed the existing classification methods in accuracy. It is utilized by classifying three motor imaginary EEG signals. Two of them are binary-class datasets, i.e., one dataset includes two-hand actions, and another includes hand and foot actions. The third dataset is multiclass, including two hands and two feet actions. In addition, the proposed granulation method overcomes the difficulties in cross-individual cases when classifying the EEG signals with a higher accuracy than the existing methods. Meanwhile, this classification procedure makes it interpretable and has a high performance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it